f occurrence of adverse events was associated with an increase in risk-standardized expenditures of $103 (95% CI, $57-$150) for AMI, $100 (95% CI, $29-$172) for HF, and $152 (95% CI, $73-$232) for pneumonia per discharge. Conclusions and Relevance Hospitals with high adverse event rates were more likely to have high 30-day episode-of-care Medicare expenditures for patients discharged with AMI, HF, or pneumonia.BACKGROUND Exact numbers of breast cancer (BC) recurrences are currently unknown at the population-level, since they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence.We present the first systematic review and meta-analysis of publications estimating BC recurrence at the population-level using algorithms based on administrative data. METHODS The systematic literature search followed Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model (GLMM) to obtain a pooled estimate of accuracy. RESULTS Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%), compared to studies using detection rules without specified model (58.8%). The GLMM for all recurrence types reported an accuracy of 92.2% (95%CI 88.4-94.8%). CONCLUSION Publications reporting algorithms for detecting BC recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify BC recurrence at the population-level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future. © The Author(s) 2020. Published by Oxford University Press. https://www.selleckchem.com/products/alw-ii-41-27.html All rights reserved. For permissions, please email journals.permissions@oup.com.N-glycanase 1 (NGLY1) deficiency, an autosomal recessive disease caused by mutations in the NGLY1 gene, is characterized by developmental delay, hypolacrima or alacrima, seizure, intellectual disability, movement disorders, and other neurological phenotypes. Because of few animal models that recapitulate these clinical signatures, the mechanisms of the onset of the disease and its progression are poorly understood, and the development of therapies is hindered. In this study, we generated the systemic Ngly1-deficient rodent model, Ngly1-/- rats, which showed developmental delay, movement disorder, somatosensory impairment, and scoliosis. These phenotypes in Ngly1-/- rats are consistent with symptoms in human patients. In accordance with the pivotal role played by NGLY1 in endoplasmic reticulum-associated degradation processes, cleaving N-glycans from misfolded glycoproteins in the cytosol before they can be degraded by the proteasome, loss of Ngly1 led to accumulation of cytoplasmic ubiquitinated proteins, a marker of misfolded proteins in the neurons of the central nervous system of Ngly1-/- rats. Histological analysis identified prominent pathological abnormalities, including necrotic lesions, mineralization, intra- and extra-cellular eosinophilic bodies, astrogliosis, microgliosis, and significant loss of mature neurons in the thalamic lateral and the medial parts of the ventral posterior nucleus and ventral lateral nucleus of Ngly1-/- rats. Axonal degradation in the sciatic nerves was also observed, as in human subjects. Ngly1-/- rats, which mimic the symptoms of human patients, will be a useful animal model for preclinical testing of therapeutic options and understanding the detailed mechanisms of NGLY1 deficiency. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.AIMS To determine whether the combination of standard electrocardiographic (ECG) markers reflecting domains of arrhythmic risk improves sudden and/or arrhythmic death (SAD) risk stratification in patients with coronary heart disease (CHD). METHODS AND RESULTS The association between ECG markers and SAD was examined in a derivation cohort (PREDETERMINE; N?=?5462) with adjustment for clinical risk factors, left ventricular ejection fraction (LVEF), and competing risk. Competing outcome models assessed the differential association of ECG markers with SAD and competing mortality. The predictive value of a derived ECG score was then validated (ARTEMIS; N?=?1900). In the derivation cohort, the 5-year cumulative incidence of SAD was 1.5% [95% confidence interval (CI) 1.1-1.9] and 6.2% (95% CI 4.5-8.3) in those with a low- and high-risk ECG score, respectively (P for Δ? less then ?0.001). A high-risk ECG score was more strongly associated with SAD than non-SAD mortality (adjusted hazard ratios = 2.87 vs. 1.38 respectively; P for Δ?=?0.003) and the proportion of deaths due to SAD was greater in the high vs. low risk groups (24.9% vs. 16.5%, P for Δ?=?0.03). Similar findings were observed in the validation cohort. The addition of ECG markers to a clinical risk factor model inclusive of LVEF improved indices of discrimination and reclassification in both derivation and validation cohorts, including correct reclassification of 28% of patients in the validation cohort [net reclassification improvement 28 (7-49%), P?=?0.009]. CONCLUSION For patients with CHD, an externally validated ECG score enriched for both absolute and proportional SAD risk and significantly improved risk stratification compared to standard clinical risk factors including LVEF. CLINICAL TRIAL REGISTRATION https//clinicaltrials.gov/ct2/show/NCT01114269. ClinicalTrials.gov ID NCT01114269. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email journals.permissions@oup.com.